Water quality response to riparian restoration in an agricultural watershed in Vermont, USA
Bibliographic record
Abstract
Achievement of management goals for Lake Champlain (Vermont/New York, USA and Quebec, Canada) will require reduction of agricultural phosphorus loads, the dominant nonpoint source in the Basin. Cost-effective phosphorus reduction strategies need reliable treatment techniques beyond basic cropland and waste management practices. The Lake Champlain Basin Agricultural Watersheds National Monitoring Program (NMP) Project evaluates the effectiveness of livestock exclusion, streambank protection, and riparian restoration practices in reducing concentrations and loads of nutrients, sediment, and bacteria in surface waters. Treatment and control watersheds in northwestern Vermont have been monitored since 1994 according to a paired-watershed design. Monitoring consists of continuous stream discharge recording, flow-proportional sampling for total P, total Kjeldahl N, and total suspended solids, grab sampling for indicator bacterial, and land use/agricultural monitoring. Strong statistical calibration between the control and treatment watersheds has been achieved. Installation of riparian fencing, protected stream crossings, and streambank bioengineering was completed in 1997. Early post-treatment data suggest significant reduction in P concentrations and loads and in bacteria counts in the treated watershed. Monitoring is scheduled to continue through 2000.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".